#marketing How to Use Predictive Analytics to Engage Customers Throughout Their Journey

As consumers become more digitally connected, their buying journeys are becoming increasingly complex: The path to purchase is no longer a linear funnel; rather, it’s a circuitous journey that continues long after the transaction has been completed.

Today’s consumers have more touch points with more brands than ever before; and so, to capture and retain their business, marketers must have a deep understanding of consumers and their intentions at every stage of the customer lifecycle.

Luckily, customers’ increased digital engagement with brands has also allowed organizations to amass more customer data, creating the opportunity to glean actionable customer insights through predictive analytics, a form of advanced analytics that uses both new and historical data to forecast future activity, behavior, and trends.

Predictive analytics has become much more prominent over the past few years as organizations look to harness their data: Gartner estimates that by 2020 predictive and prescriptive analytics will attract 40% of enterprises’ net new investment in business intelligence (BI) and analytics.

Today’s marketers should apply predictive analytics at every stage of the customer journey, from raising awareness, to educating prospects, to completing the transaction, to enhancing customer service and beyond. Doing so will help marketers anticipate their customers’ needs and desires at every moment, so that they personalize engagement with each customer.

However, many marketers are likely left wondering how to leverage predictive analytics. What data systems and services need to be in place? And how, exactly, can predictive analytics be applied at various stages in the customer journey?

Laying the Foundation for Personalized Engagement

To effectively harness the power of predictive analytics throughout the entire customer journey, organizations must invest in a customer relationship management (CRM) platform that supports advanced analytics and integrations with other applications.

At its core, CRM software helps businesses store and manage customer information, such as contact information, purchase histories, demographics, and interaction information. Many CRM vendors are undergoing massive changes to integrate and enable tools that allow businesses to deliver more predictive and personalized customer experiences. Salesforce and Microsoft, for example, are making substantial investments in artificial intelligence (AI) to make their platforms more intelligent. These vendors offer predictive analytics as an embedded feature in their products and also as an add-on to their existing platforms.

If your organization relies on an older CRM system, you may want to talk to IT about replacing it, or consider licensing separate software that can be integrated into your existing CRM.

Stage 1: Targeting

Identifying the right prospects to reach out to is the first step in every marketing campaign. It’s also arguably the most important step. Even if every other aspect of a marketers’ campaign is outstanding, if it’s not reaching the right audience it will fall flat.

To curate a highly targeted, qualified prospect list, marketers should build their lists from machine-learning-based predictive models, which deliver significantly more accurate data intelligence than traditional models that use a simplistic rules-based approach.

New, innovative machine-learning models learn from and leverage the intelligence that resides in the CRM, such as historical information about who bought products or services in the past. Accordingly, marketers must start with a prospect list—for example, a list of customers who bought your product, responded to a previous email marketing campaign, or attended a webinar. The list must then be appended with additional data attributes to make it more intelligent. Next, it must go through multiple machine-learning algorithms so the data can be sorted in an intelligent manner; this usually involves assigning each prospect a score so marketers can quickly make sense of the information and use it to generate a targeted prospect list.

If you do not have data science training, you’ll need to enlist the help of a data scientist or use a self-serve, automated predictive analytics platform that can generate a predictive model for you. In many cases, using a self-serve platform is a better option because it is more cost-effective and allows you to manage the process yourself without having to wait for a data scientist to deliver the information.

Once you have a system in place, you can use it to apply predictive analytics to subsequent stages in the customer journey.

Stage 2: Education

Once you have a prospect’s attention, to seal the deal it’s important that your next interactions with them cater to their specific needs and desires. Predictive analytics can help you do so in a couple of ways:

First, marketers can apply predictive analytics to display personalized webpages based on a consumer’s personal preferences. This is achieved by applying machine-learning algorithms that track online habits that help marketers create personal online experiences.

Second, when marketers follow up via phone or email, they can personalize the interaction based on knowledge from previous interactions or insights derived from external data. Machine-learning can be applied to cull through external consumer and business data points and apply them to existing customer listings within the CRM. This approach helps the marketer understand who the prospect is outside of their professional life—such as where they went to school or whether they like golf—to help build a deeper relationship with them.

Stages 3 and 4: Purchase and Cross-Sell/Upsell

After you close the deal with a customer, the next step is to ensure they remain a satisfied customer. When done right, cross-selling and upselling can provide better value for customers while also increasing profitability for your company. The key is to make relevant product recommendations that match a customer’s needs and desires.

Predictive analytics can be applied to match product offers to each customer based on demographic data, purchase history, and data from previous customer interactions—ensuring each product recommendation is valuable and relevant, to optimize sales and customer service.

Stage 5: Satisfaction

For a company to grow, it must exceed its churn rate, which is the percentage of customers who discontinue their subscription to a service within a given period of time.

Using predictive analytics, marketers can forecast which customers are likely to churn; marketers can then apply retention campaign dollars more effectively. For example, if you can predict that a first-time customer won’t return, you can target that customer with a nurturing campaign offering discounts or free trials to entice the customer to stay.

Predictive analytics also enables marketers to monitor and course-correct in real-time by watching metrics such as sales, retention, and churn in the CRM system.

Conclusion

As consumers’ buying journeys become more complex and their demand for personalized experiences increases, predictive analytics offer a way for marketers to keep up by giving them a deeper understanding of their customers and allowing them to sell more effectively at every stage of the customer journey.